This article describes a method to develop a generic approach to acquire navigation capabilities for the standard platform of the IMAV indoor competition: the Parrot AR.Drone. Our development is partly based on simulation, which requires both a realistic sensor and motion model. The AR.Drone simulation model is described and validated. Furthermore, this article describes how a visual map of the indoor environment can be made, including the effect of sensor noise. This visual map consists of a texture map and a feature map. The texture map is used for human navigation and the feature map is used by the AR.Drone to localize itself. To do so, a localization method is presented. An experiment demonstrates how well the localization works for circumstances encountered during the IMAV competition.
References
1.
StederB.GrisettiG.StachnissC., and BurgardW.. Visual SLAM for flying vehicles. Robotics, IEEE Transactions on, 24(5): 1088–1093, 2008.
2.
BayHerbertEssAndreasTuytelaarsTinne, and Van GoolLuc. Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3): 346–359, 2008.
3.
GrisettiG.GrzonkaS.StachnissC.PfaffP., and BurgardW.. Efficient estimation of accurate maximum likelihood maps in 3d. In IROS, pages 3472–3478, San Diego, CA (USA), 2007.
4.
CaballeroF.MerinoL.FerruzJ., and OlleroA.. Unmanned Aerial Vehicle Localization Based on Monocular Vision and Online Mosaicking. Journal of Intelligent and Robotic Systems, 55(4): 323–343, 2009.
5.
BachrachAbrahamHeRuijie, and RoyNicholas. Autonomous flight in unknown indoor environments. International Journal of Micro Air Vehicles, 1(4): 217–228, December 2009.
6.
StowersJohnBainbridge-SmithAndrewHayesMichael, and MillsSteven. Optical flow for heading estimation of a quadrotor helicopter. International Journal of Micro Air Vehicles, 1(4): 229–239, December 2009.
7.
JovanovE.MilosevicM.TillyR.TruexM., and JonesC.. Autonomous Personnel Tracking Help AR.Drone. In International Micro Air Vehicle Conference, May 2011.
8.
BillsCooperChenJoyce, and SaxenaAshutosh. Autonomous mav flight in indoor environments using single image perspective cues. In International Conference on Robotics and Automation (ICRA), 2011.
9.
MeierL.TanskanenP.FraundorferF., and PollefeysM.. Pixhawk: A system for autonomous flight using onboard computer vision. In IEEE Int. Conf. Robotics and Automation (ICRA'11), May 2011.
10.
MichaelN.MellingerD.LindseyQ., and KumarV.. The grasp multiple micro-uav testbed. Robotics Automation Magazine, IEEE, 17(3): 56–65, sept. 2010.
11.
Workshop on robots, games, and research: Success stories in usarsim. In BalakirkyStephenCarpinStefano, and LewisMike, editors, Proceedings of the International Conference on Intelligent Robots and Systems (IROS2009). IEEE, October 2009.
12.
CarpinStefanoWangJijunLewisMichaelBirkAndreas, and JacoffAdam. High fidelity tools for rescue robotics: Results and perspectives. In BredenfeldAnsgarJacoffAdamNodaItsuki, and TakahashiYasutake, editors, RoboCup2005: Robot Soccer World Cup IX, volume 4020 of Lecture Notes in Computer Science, pages 301–311. SpringerBerlin / Heidelberg, 2006.
13.
YechoutT.MorrisS.BossertD., and HallgrenW.. Introduction to Aircraft Flight Mechanics: Performance, Static Stability, Dynamic Stability, and Classical Feedback Control. American Institute of Aeronautics and Astronautics, Reston, VA, 2003.
14.
HoffmannGabriel M.HuangHaomiaoWaslSteven L., and TomlinClaire J.. Quadrotor helicopter flight dynamics and control: Theory and experiment. In Proc. of the AIAA Guidance, Navigation and Control Conference, 2007.
15.
VisserArnoudDijkshoornNickvan der VeenMartijn, and JurriaansRobrecht. Closing the gap between simulation and reality in the sensor and motion models of an autonomous ar.drone. In Proceedings of the International Micro Air Vehicle Conference and Flight Competition (IMAV 11), September 2011.
16.
JulierS.J. and UhlmannJ.K.. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3): 401–422, 2004.
17.
ZhangZ.. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22: 1330–1334, 2000.
18.
BouguetJean-Yves. Visual methods for three-dimensional modeling. PhD thesis, California Institute of Technology, May 1999.
19.
FischlerM.A. and BollesR.C.. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381–395, 1981.
20.
KabschW.. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, 32(5): 922–923, Sep 1976.
21.
JacoffAdamMessinaElenaHuangHui-MinVirtsAnn, and DownsAnthony. Standard Test Methods for Response Robots. ASTM International Committee on Homeland Security Applications, January 2010. subcommittee E54.08.01.
22.
JurriaansRobrecht. Flow based obstacle avoidance for real world autonomous aerial navigation tasks. Bachelor's thesis, Universiteit van Amsterdam, August 2011.
23.
van der VeenMartijn. Optimizing artificial force fields for autonomous drones in the pylon challenge using reinforcement learning. Bachelor's thesis, Universiteit van Amsterdam, July 2011.
